| | --- |
| | license: mit |
| | base_model: hongpingjun98/BioMedNLP_DeBERTa |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - sem_eval_2024_task_2 |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: BioMedNLP_DeBERTa_all_updates |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: sem_eval_2024_task_2 |
| | type: sem_eval_2024_task_2 |
| | config: sem_eval_2024_task_2_source |
| | split: validation |
| | args: sem_eval_2024_task_2_source |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.655 |
| | - name: Precision |
| | type: precision |
| | value: 0.6714791459232217 |
| | - name: Recall |
| | type: recall |
| | value: 0.655 |
| | - name: F1 |
| | type: f1 |
| | value: 0.6465073388150311 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # BioMedNLP_DeBERTa_all_updates |
| | |
| | This model is a fine-tuned version of [hongpingjun98/BioMedNLP_DeBERTa](https://huggingface.co/hongpingjun98/BioMedNLP_DeBERTa) on the sem_eval_2024_task_2 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.4673 |
| | - Accuracy: 0.655 |
| | - Precision: 0.6715 |
| | - Recall: 0.655 |
| | - F1: 0.6465 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 20 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.3757 | 1.0 | 115 | 0.6988 | 0.7 | 0.7020 | 0.7 | 0.6992 | |
| | | 0.3965 | 2.0 | 230 | 0.7320 | 0.695 | 0.7259 | 0.6950 | 0.6842 | |
| | | 0.3603 | 3.0 | 345 | 0.7736 | 0.7 | 0.7338 | 0.7 | 0.6888 | |
| | | 0.2721 | 4.0 | 460 | 0.8780 | 0.665 | 0.6802 | 0.665 | 0.6578 | |
| | | 0.4003 | 5.0 | 575 | 0.9046 | 0.655 | 0.6796 | 0.655 | 0.6428 | |
| | | 0.2773 | 6.0 | 690 | 0.9664 | 0.7 | 0.7053 | 0.7 | 0.6981 | |
| | | 0.2465 | 7.0 | 805 | 1.0035 | 0.67 | 0.6845 | 0.67 | 0.6634 | |
| | | 0.3437 | 8.0 | 920 | 1.0087 | 0.665 | 0.6780 | 0.665 | 0.6588 | |
| | | 0.1175 | 9.0 | 1035 | 1.2598 | 0.675 | 0.6780 | 0.675 | 0.6736 | |
| | | 0.155 | 10.0 | 1150 | 1.3976 | 0.69 | 0.7038 | 0.69 | 0.6847 | |
| | | 0.1013 | 11.0 | 1265 | 1.3761 | 0.67 | 0.6757 | 0.6700 | 0.6673 | |
| | | 0.1664 | 12.0 | 1380 | 1.5027 | 0.695 | 0.6950 | 0.695 | 0.6950 | |
| | | 0.0847 | 13.0 | 1495 | 1.8199 | 0.685 | 0.6973 | 0.685 | 0.68 | |
| | | 0.0856 | 14.0 | 1610 | 1.8299 | 0.66 | 0.6783 | 0.6600 | 0.6511 | |
| | | 0.1053 | 15.0 | 1725 | 2.0431 | 0.665 | 0.6852 | 0.665 | 0.6556 | |
| | | 0.0958 | 16.0 | 1840 | 1.9203 | 0.7 | 0.7040 | 0.7 | 0.6985 | |
| | | 0.0344 | 17.0 | 1955 | 2.1390 | 0.665 | 0.6780 | 0.665 | 0.6588 | |
| | | 0.014 | 18.0 | 2070 | 2.3609 | 0.655 | 0.6692 | 0.655 | 0.6476 | |
| | | 0.0085 | 19.0 | 2185 | 2.4310 | 0.65 | 0.6671 | 0.65 | 0.6408 | |
| | | 0.0285 | 20.0 | 2300 | 2.4673 | 0.655 | 0.6715 | 0.655 | 0.6465 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.0 |
| |
|
